Influence of biofilm growth age, media and antibiotics exposure time on Staphylococcus aureus and Pseudomonas aeruginosa biofilm removal in vitro
Bibliographic record
Abstract
Abstract Biofilm is known to be tolerant towards antibiotics and difficult to eradicate. Numerous studies have reported Minimum Biofilm Eradication Concentration (MBEC) values of antibiotics for many known biofilm pathogens. However, the experimental parameters applied in these studies differ considerably, and often the rationale behind the experimental design are not well described. This makes it difficult to compare the findings. To demonstrate the importance of experimental parameters, we investigated the influence of biofilm growth age, antibiotic treatment duration and growth media on biofilm eradication in this study. The commonly used biofilm model Calgary biofilm device was used to grow 24 h and 72 h biofilms of Staphylococcus aureus and Pseudomonas aeruginosa , which were treated with time-dependent vancomycin and concentration-dependent tobramycin, respectively. Two common bacteriological growth media Tryptic Soy Broth (TSB) and Cation-adjusted Mueller Hinton Broth (CaMHB) were tested. We found for both species that biofilms were more difficult to kill in TSB than in CaMHB. Furthermore, young biofilms (24 h) were easier to eradicate than old biofilms (72 h). In agreement with vancomycin being time-dependent, extension of the vancomycin exposure increased killing of S. aureus biofilms. Tobramycin treatment of 24 h P. aeruginosa biofilms was found concentration-dependent and time-independent, however, increasing killing was indicated for 72 h P. aeruginosa biofilms. This study demonstrated biofilm removal efficacy was influenced by media, biofilm age and antibiotics treatment duration. It is therefore necessary to taking these parameters into consideration when designing experiments.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".